Perbandingan harga saham, volume transaksi dan return saham sebelum dan sesudah terjadi pandemi covid-19 pada PT Agung Podomoro Land, Tbk

The first Covid-19 case in Indonesia was announced on March 2, 2020. This study aims to determine whether there is a significant difference in stock prices, stock transaction volume and stock returns due to the COVID-19 pandemic (case study at PT. Agung Podomoro Land, Tbk). This research data was taken 90 days before and 90 days after the announcement of the first case of COVID-19 in Indonesia. The data was processed by paired sample t-test, using SPSS version 20. From the results of data processing, it was shown that there was a significant difference in stock prices before and after the announcement of the first case of covid-19 in Indonesia. This is indicated by a significance value of 0.000 < 0.05 where the stock price has decreased compared to before the Covid-19 case. Meanwhile, the volume of stock transactions also showed a significant difference with a significance value of 0.007 <0.05, where the volume of stock transactions after the announcement showed a decrease. Likewise, stock returns show a significant difference with a significance value of 0.025 < 0.05 where stock returns have decreased after the announcement of the first case of covid-10 in Indonesia.

Algorithmic Methods of Hidden Communication Through Buy Orders and Sales on International Stock Exchanges

This paper presents the results of the author’s research on the design of hidden communication algorithms employed in the context of global exchange services. The solutions proposed enable communication between trading participants without use of such traditional communication routes as email, telephone, instant messaging, discussion forums, etc. The solutions described are based on modification of entries in the exchange tables of orders and transactions. Through modification of the entries associated with share buy and sell orders, a secret channel can be constructed through which hidden messages can be sent. Such messages could, for example, be used to manipulate stock prices by an organized group of people. The proposed solutions can classified as steganographic methods where the message carrier is a stock transaction or stock order table, in which a message is embedded by means of algorithmic modification of buy and sell records. Also presented are specific proposals for static, dynamic, and mixed static-dynamic solutions based on the results of the author’s research. In the static methods group, an imperceptible communication channel is formed through a series of asynchronous modifications that create a complete, readable message that is present for a relatively long time. In dynamic methods, the embedded message is synchronized in time and creates a sequence of events that create statements. The third group of methods presented, mixed methods, use static and dynamic techniques to construct hidden messages. In particular, the method of extreme orders (MEO), mono-table method (MTM), multi-table method (MUTM), method of price-indexed vectors (MPIV), method of quantity-indexed vectors (MQIV), clustered order method (COM), distributed order method (DOM), position-encoded method (PEM), method with quantity coding (MQC), method with error correction (MEC), method limited to buy orders (MLBO), method limited to sell orders (MLSO), and self-synchronizing method (SSM) are presented. The solutions presented in this work can be applied practically in any publicly available stock trading system in which order tables are available. The algorithms presented in the paper were implemented and verified on a real trading service, and the research software used was implemented based on the API provided by the brokerage office.

Earnings Quality, Asymmetric Information, and Market Response : Insights from Indonesia

This study aims to investigate the impact of earnings quality and asymmetric information on market reaction which is proxied by cumulative abnormal return (CAR). This study also attempts to analyze whether good governance is playing an important role in moderating the effects of those two independent variables on CAR. By using purposive sampling method, this study covers 125 manufacturing companies listed on Indonesian Stock Exchange for 2017-2018 period to reveal above agenda. By using generalize least square (GLS) fixed effect model, it is found that reported earnings on financial statement have meaningful information for investors. By this, investors could expect stock return by relying on this earning information. Meanwhile, asymmetric information also matters for investors to make decisions regarding stock transaction. However, independence commissioner which is predicted to have an important role for stock performance, is not proved as moderating variable in this study.

Investor behavior based on personality and company life cycle

This study aims to ensure that the behavior of individual stock investors has the best pattern in accordance with the existing personality, and see the business cycle starting from introduction, growth, maturity, decline in LQ-45 from the proxy of operational cash flows, investment cash flows, and funding cash flows. This study uses the Analytical Hierarchy Process (AHP) method to see what behavior tends to be used in carrying out shareholding transactions. The results of this study create a behavior model of individual stock investors. Individual stock investors in Indonesia tend to have precision personality types in making stock transaction decisions. This type is a dominant combination, stability, and compliance. The model of individual stock investor behavior starts from making a price target; has a relationship between investors to exchange information related to shares monitored; valuation of shares to be transacted. In addition, the behavior of individual stock investors tends to have accounting information and corporate environmental concerns. Behavioral model Individual stock investors who already have two years or more experience in stock transactions, will have a unique pattern of technical analysis and their own fundamental analysis.

Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework

This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and FGLS in terms of eliminating serial correlations, but the estimator can be sensitive to start value. Hence, two-stage QML has been suggested. In empirical estimation on two stock transaction data for Ericsson and AstraZeneca, the 2SQML turns out relatively more efficient than CLS and FGLS. The empirical results suggest that both of the series have long memory properties that imply that the impact of macroeconomic news or rumors in one point of time has a persistence impact on future transactions.